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A Python package that implements model-agnostic pre-and post-processing to mitigate unfairness in machine learning prediction

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Adjusting Data and Outcomes for AI Fairness (FairData)

A Python package that implements model-agnostic pre-and post-processing to mitigate unfairness in machine learning prediction tasks. Fix machine learning bias by adjusting the underlying data or model outputs.

All regulation:

US:

  • Civil Rights Acts of 1964 and 1991
  • Americans with Disabilities Act
  • Genetic Information Nondiscrimination Act
  • Health Insurance Portability and Accountability Act
  • Equal Credit Opportunity Act (ECOA)
  • Fair Credit Reporting Act (FCRA)
  • Fair Housing Act
  • Federal Reserve Supervision and Regulation (SR) Letter 11-7

EU

  • European Union (EU) General Data Protection Regulation (GDPR) Article 22

UK:

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A Python package that implements model-agnostic pre-and post-processing to mitigate unfairness in machine learning prediction

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